EEG signal processing

Saeid Sanei, J. A. Chambers0470025816, 9780470025819

Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services.
Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods.
Additionally, expect to find: explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals; an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs; reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals; coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon; descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing. The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.

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Table of contents :
EEG SIGNAL PROCESSING……Page 3
Contents……Page 7
Preface……Page 11
List of Abbreviations……Page 13
List of Symbols……Page 19
1.1 History……Page 25
1.2 Neural Activities……Page 28
1.3 Action Potentials……Page 29
1.4 EEG Generation……Page 31
1.5 Brain Rhythms……Page 34
1.6 EEG Recording and Measurement……Page 37
1.6.1 Conventional Electrode Positioning……Page 39
1.6.2 Conditioning the Signals……Page 42
1.7 Abnormal EEG Patterns……Page 44
1.9.1 Dementia……Page 46
1.9.2 Epileptic Seizure and Nonepileptic Attacks……Page 48
1.9.3 Psychiatric Disorders……Page 52
1.9.4 External Effects……Page 53
1.10 Summary and Conclusions……Page 54
References……Page 55
2 Fundamentals of EEG Signal Processing……Page 59
2.1 EEG Signal Modelling……Page 60
2.1.1 Linear Models……Page 66
2.1.2 Nonlinear Modelling……Page 69
2.1.3 Generating EEG Signals Based on Modelling the Neuronal Activities……Page 71
2.3 Nonstationarity……Page 74
2.4 Signal Segmentation……Page 75
2.5 Signal Transforms and Joint TimeFrequency Analysis……Page 79
2.5.1 Wavelet Transform……Page 82
2.5.2 Ambiguity Function and the WignerVille Distribution……Page 88
2.6 Coherency, Multivariate Autoregressive (MVAR) Modelling, and Directed Transfer Function (DTF)……Page 91
2.7.2 Kolmogorov Entropy……Page 95
2.7.3 Lyapunov Exponents……Page 96
2.7.4 Plotting the Attractor Dimensions from the Time Series……Page 98
2.7.5 Estimation of Lyapunov Exponents from the Time Series……Page 99
2.7.6 Approximate Entropy……Page 101
2.7.7 Using the Prediction Order……Page 102
2.8 Filtering and Denoising……Page 103
2.9 Principal Component Analysis……Page 107
2.9.1 Singular-Value Decomposition……Page 108
2.10 Independent Component Analysis……Page 110
2.10.1 Instantaneous BSS……Page 114
2.10.2 Convolutive BSS……Page 119
2.10.3 Sparse Component Analysis……Page 122
2.10.4 Nonlinear BSS……Page 123
2.10.5 Constrained BSS……Page 124
2.11 Application of Constrained BSS: Example……Page 126
2.12 Signal Parameter Estimation……Page 128
2.13 Classification Algorithms……Page 129
2.13.1 Support Vector Machines……Page 130
2.13.2 The k-Means Algorithm……Page 138
2.14 Matching Pursuits……Page 141
2.15 Summary and Conclusions……Page 142
References……Page 143
3 Event-Related Potentials……Page 151
3.1 Detection, Separation, Localization, and Classificationof P300 Signals……Page 155
3.1.2 Estimating Single Brain Potential Components by Modelling ERP Waveforms……Page 156
3.1.3 Source Tracking……Page 159
3.1.4 Localization of the ERP……Page 161
3.1.5 TimeFrequency Domain Analysis……Page 166
3.1.6 Adaptive Filtering Approach……Page 169
3.1.7 Prony’s Approach for Detection of P300 Signals……Page 172
3.1.8 Adaptive TimeFrequency Methods……Page 175
3.2 Brain Activity Assessment Using ERP……Page 177
3.3 Application of P300 to BCI……Page 178
3.4 Summary and Conclusions……Page 179
References……Page 180
4 Seizure Signal Analysis……Page 185
4.1.1 Adult Seizure Detection……Page 190
4.1.2 Detection of Neonate Seizure……Page 195
4.2 Chaotic Behaviour of EEG Sources……Page 199
4.3 Predictability of Seizure from the EEGs……Page 200
4.4 Fusion of EEGfMRI Data for Seizure Prediction……Page 213
References……Page 215
5.1 Introduction……Page 221
5.1.2 Dipole Assumption……Page 222
5.2.2 MUSIC Algorithm……Page 225
5.2.3 LORETA Algorithm……Page 228
5.2.5 Standardized LORETA……Page 230
5.2.6 Other Weighted Minimum Norm Solutions……Page 232
5.2.7 Evaluation Indices……Page 233
5.2.8 Joint ICALORETA Approach……Page 234
5.2.9 Partially Constrained BSS Method……Page 235
5.3 Determination of the Number of Sources……Page 237
References……Page 239
6 Sleep EEG……Page 243
6.1.1 NREM Sleep……Page 244
6.2 The Influence of Circadian Rhythms……Page 246
6.4 Psychological Effects……Page 248
6.5.1 Detection of the Rhythmic Waveforms and Spindles IncorporatingBlind Source Separation……Page 249
6.5.2 Application of Matching Pursuit……Page 251
6.5.3 Detection of Normal Rhythms and Spindles using Higher Order Statistics……Page 252
6.5.4 Application of Neural Networks……Page 255
6.5.5 Model-Based Analysis……Page 256
6.5.6 Hybrid Methods……Page 258
References……Page 259
7 BrainComputer Interfacing……Page 263
7.1 State of the Art in BCI……Page 264
7.1.1 ERD and ERS……Page 267
7.1.2 Transient Beta Activity after the Movement……Page 268
7.2.1 Preprocessing of the EEGs……Page 269
7.3 Multidimensional EEG Decomposition……Page 272
7.3.2 Parallel Factor Analysis……Page 275
7.6 Multivariant Autoregressive (MVAR) Modellingand Coherency Maps……Page 279
7.7 Estimation of Cortical Connectivity……Page 281
7.8 Summary and Conclusions……Page 284
References……Page 285
Index……Page 291